{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 1903073\n",
      "Most common words (+UNK) [['UNK', 0], ('。', 149620), ('\\n', 117070), ('，', 108451), ('、', 19612)]\n",
      "Sample data [1503, 1828, 2, 2, 40, 613, 47, 9, 111, 117] ['潘', '阆', '\\n', '\\n', '酒', '泉', '子', '（', '十', '之']\n",
      "1828 阆 -> 2 \n",
      "\n",
      "1828 阆 -> 1503 潘\n",
      "2 \n",
      " -> 1828 阆\n",
      "2 \n",
      " -> 2 \n",
      "\n",
      "2 \n",
      " -> 40 酒\n",
      "2 \n",
      " -> 2 \n",
      "\n",
      "40 酒 -> 2 \n",
      "\n",
      "40 酒 -> 613 泉\n",
      "WARNING:tensorflow:From <ipython-input-1-69a4fe050fb0>:212: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "keep_dims is deprecated, use keepdims instead\n",
      "Initialized\n",
      "Average loss at step  0 :  272.7347412109375\n",
      "[15323  4557 25412 28993 34636 21518 14936  2881]\n",
      "Nearest to 秋: m,\n",
      "Nearest to 秋: m, ？,\n",
      "[28146 10371 19979 44578 31166 32226 45929 25435]\n",
      "[23976 26802 11599 36081  5313 43264 13217 36256]\n",
      "Nearest to 行: 恧,\n",
      "[40139 27072 28007 32870 48605  4329 30538    30]\n",
      "Nearest to 寒: 枳,\n",
      "Nearest to 寒: 枳, 何,\n",
      "[12162 12018 49930 17624  3529 31432 34384 10009]\n",
      "Nearest to 处: 槿,\n",
      "[36508 28741 47708 24457 37395 42490 30640 23562]\n",
      "[34106 12886 42032  9777  1782 13293 31414 40234]\n",
      "Nearest to 上: 义,\n",
      "[33372 22397 13454 32707  5605 32798 24728  9884]\n",
      "Nearest to 人: 裤,\n",
      "[ 4517 16464 13333 25902 18147 36100  1069 28699]\n",
      "Nearest to （: 伛,\n",
      "Nearest to （: 伛, 牛,\n",
      "[ 4116 37578 11981 47413 17917 17683 18013  4167]\n",
      "Nearest to 云: 椎,\n",
      "Nearest to 云: 椎, 鉏,\n",
      "[13227 32437 10797 41809 10118 18757  1837 17664]\n",
      "Nearest to 似: 禅,\n",
      "[24717  5613 14857 17992 13370 26321 34484 19972]\n",
      "Nearest to 东: 逑,\n",
      "[ 7358 44598 21453  4793 24307 38603 43220 37395]\n",
      "Nearest to 愁: 逖,\n",
      "[31716  8771 46328  5918 39944 17967 18285  5177]\n",
      "Nearest to 梦: 饘,\n",
      "Nearest to 梦: 饘, 徯,\n",
      "[15165 20971  6904 40407 45743 49273   830 42730]\n",
      "Nearest to 中: 赵,\n",
      "[41530  8913 28417 19831  2554 26759 12627 20748]\n",
      "Nearest to 声: 畏,\n",
      "Average loss at step  2000 :  105.00977385616302\n",
      "Average loss at step  4000 :  45.442493760824206\n",
      "Average loss at step  6000 :  27.738125393629073\n",
      "Average loss at step  8000 :  18.215814413070678\n",
      "Average loss at step  10000 :  13.57522370004654\n",
      "Average loss at step  12000 :  10.215563371658325\n",
      "Average loss at step  14000 :  8.0785270318985\n",
      "Average loss at step  16000 :  6.9098348624706265\n",
      "Average loss at step  18000 :  6.516593479990959\n",
      "Average loss at step  20000 :  5.57424994969368\n",
      "[1668 2041 1375 1182 2765  957  324 2541]\n",
      "Nearest to 秋: 箭,\n",
      "Nearest to 秋: 箭, 旬,\n",
      "Nearest to 秋: 箭, 旬, 胭,\n",
      "Nearest to 秋: 箭, 旬, 胭, 鳞,\n",
      "Nearest to 秋: 箭, 旬, 胭, 鳞, 酡,\n",
      "Nearest to 秋: 箭, 旬, 胭, 鳞, 酡, 阔,\n",
      "Nearest to 秋: 箭, 旬, 胭, 鳞, 酡, 阔, 转,\n",
      "Nearest to 秋: 箭, 旬, 胭, 鳞, 酡, 阔, 转, 胶,\n",
      "[  85  560   87 2405 1076  163 3232 1410]\n",
      "Nearest to 为: 见,\n",
      "Nearest to 为: 见, 忘,\n",
      "Nearest to 为: 见, 忘, 与,\n",
      "Nearest to 为: 见, 忘, 与, 峦,\n",
      "Nearest to 为: 见, 忘, 与, 峦, 羡,\n",
      "Nearest to 为: 见, 忘, 与, 峦, 羡, 同,\n",
      "Nearest to 为: 见, 忘, 与, 峦, 羡, 同, 憩,\n",
      "Nearest to 为: 见, 忘, 与, 峦, 羡, 同, 憩, 枯,\n",
      "[2275  631  961 3302 1361 1148 1965  126]\n",
      "Nearest to 行:  ,\n",
      "Nearest to 行:  , 惟,\n",
      "Nearest to 行:  , 惟, 耳,\n",
      "Nearest to 行:  , 惟, 耳, 嵯,\n",
      "Nearest to 行:  , 惟, 耳, 嵯, 芦,\n",
      "Nearest to 行:  , 惟, 耳, 嵯, 芦, 妒,\n",
      "Nearest to 行:  , 惟, 耳, 嵯, 芦, 妒, 嫁,\n",
      "Nearest to 行:  , 惟, 耳, 嵯, 芦, 妒, 嫁, 平,\n",
      "[ 338 2275 3944 1462    3 2416 2424 3611]\n",
      "Nearest to 寒: 余,\n",
      "Nearest to 寒: 余,  ,\n",
      "Nearest to 寒: 余,  , 恃,\n",
      "Nearest to 寒: 余,  , 恃, 朋,\n",
      "Nearest to 寒: 余,  , 恃, 朋, ，,\n",
      "Nearest to 寒: 余,  , 恃, 朋, ，, 矫,\n",
      "Nearest to 寒: 余,  , 恃, 朋, ，, 矫, 怡,\n",
      "Nearest to 寒: 余,  , 恃, 朋, ，, 矫, 怡, 侠,\n",
      "[ 774 3529  104  789 1021 2833 3174 3944]\n",
      "Nearest to 处: 亲,\n",
      "Nearest to 处: 亲, 槿,\n",
      "Nearest to 处: 亲, 槿, □,\n",
      "Nearest to 处: 亲, 槿, □, 密,\n",
      "Nearest to 处: 亲, 槿, □, 密, 诏,\n",
      "Nearest to 处: 亲, 槿, □, 密, 诏, 戒,\n",
      "Nearest to 处: 亲, 槿, □, 密, 诏, 戒, 铗,\n",
      "Nearest to 处: 亲, 槿, □, 密, 诏, 戒, 铗, 恃,\n",
      "[ 501  713  679 1225 1178 2909 3317  316]\n",
      "Nearest to 雨: 角,\n",
      "Nearest to 雨: 角, 鸟,\n",
      "Nearest to 雨: 角, 鸟, 恩,\n",
      "Nearest to 雨: 角, 鸟, 恩, 探,\n",
      "Nearest to 雨: 角, 鸟, 恩, 探, 兄,\n",
      "Nearest to 雨: 角, 鸟, 恩, 探, 兄, 堞,\n",
      "Nearest to 雨: 角, 鸟, 恩, 探, 兄, 堞, 荠,\n",
      "Nearest to 雨: 角, 鸟, 恩, 探, 兄, 堞, 荠, 淡,\n",
      "[1782 2479  762 2008  372 1762  106  440]\n",
      "Nearest to 上: 义,\n",
      "Nearest to 上: 义, 襦,\n",
      "Nearest to 上: 义, 襦, 报,\n",
      "Nearest to 上: 义, 襦, 报, 鲛,\n",
      "Nearest to 上: 义, 襦, 报, 鲛, 管,\n",
      "Nearest to 上: 义, 襦, 报, 鲛, 管, 瓮,\n",
      "Nearest to 上: 义, 襦, 报, 鲛, 管, 瓮, 下,\n",
      "Nearest to 上: 义, 襦, 报, 鲛, 管, 瓮, 下, 浣,\n",
      "[ 223  957 1324  449    4 1571 2066 3063]\n",
      "Nearest to 人: 曾,\n",
      "Nearest to 人: 曾, 阔,\n",
      "Nearest to 人: 曾, 阔, 斋,\n",
      "Nearest to 人: 曾, 阔, 斋, 屏,\n",
      "Nearest to 人: 曾, 阔, 斋, 屏, 、,\n",
      "Nearest to 人: 曾, 阔, 斋, 屏, 、, 沼,\n",
      "Nearest to 人: 曾, 阔, 斋, 屏, 、, 沼, 漂,\n",
      "Nearest to 人: 曾, 阔, 斋, 屏, 、, 沼, 漂, 沥,\n",
      "[   3 1973 3712  440 2170 1053 1606 1991]\n",
      "Nearest to （: ，,\n",
      "Nearest to （: ，, 昆,\n",
      "Nearest to （: ，, 昆, 沅,\n",
      "Nearest to （: ，, 昆, 沅, 浣,\n",
      "Nearest to （: ，, 昆, 沅, 浣, 苕,\n",
      "Nearest to （: ，, 昆, 沅, 浣, 苕, 殷,\n",
      "Nearest to （: ，, 昆, 沅, 浣, 苕, 殷, 溶,\n",
      "Nearest to （: ，, 昆, 沅, 浣, 苕, 殷, 溶, 骄,\n",
      "[4116    1 2003 1804   69 2275 2187  364]\n",
      "Nearest to 云: 椎,\n",
      "Nearest to 云: 椎, 。,\n",
      "Nearest to 云: 椎, 。, 棱,\n",
      "Nearest to 云: 椎, 。, 棱, 桨,\n",
      "Nearest to 云: 椎, 。, 棱, 桨, 烟,\n",
      "Nearest to 云: 椎, 。, 棱, 桨, 烟,  ,\n",
      "Nearest to 云: 椎, 。, 棱, 桨, 烟,  , 盆,\n",
      "Nearest to 云: 椎, 。, 棱, 桨, 烟,  , 盆, 面,\n",
      "[  28 1837  132 1584   97 2865  155   29]\n",
      "Nearest to 似: 如,\n",
      "Nearest to 似: 如, 禅,\n",
      "Nearest to 似: 如, 禅, 到,\n",
      "Nearest to 似: 如, 禅, 到, 区,\n",
      "Nearest to 似: 如, 禅, 到, 区, 多,\n",
      "Nearest to 似: 如, 禅, 到, 区, 多, 俾,\n",
      "Nearest to 似: 如, 禅, 到, 区, 多, 俾, 开,\n",
      "Nearest to 似: 如, 禅, 到, 区, 多, 俾, 开, 是,\n",
      "[  56 3336   78 2275 2502   93  490 1223]\n",
      "Nearest to 东: 西,\n",
      "Nearest to 东: 西, 柝,\n",
      "Nearest to 东: 西, 柝, 前,\n",
      "Nearest to 东: 西, 柝, 前,  ,\n",
      "Nearest to 东: 西, 柝, 前,  , 汞,\n",
      "Nearest to 东: 西, 柝, 前,  , 汞, 流,\n",
      "Nearest to 东: 西, 柝, 前,  , 汞, 流, 汉,\n",
      "Nearest to 东: 西, 柝, 前,  , 汞, 流, 汉, 续,\n",
      "[1586  741 1145   64 1943 4141 3142  406]\n",
      "Nearest to 愁: 规,\n",
      "Nearest to 愁: 规, 扫,\n",
      "Nearest to 愁: 规, 扫, 涛,\n",
      "Nearest to 愁: 规, 扫, 涛, 更,\n",
      "Nearest to 愁: 规, 扫, 涛, 更, 诚,\n",
      "Nearest to 愁: 规, 扫, 涛, 更, 诚, 戮,\n",
      "Nearest to 愁: 规, 扫, 涛, 更, 诚, 戮, 演,\n",
      "Nearest to 愁: 规, 扫, 涛, 更, 诚, 戮, 演, 怕,\n",
      "[2162 2275 1790  857 2373 2525  408 4264]\n",
      "Nearest to 梦: 蛟,\n",
      "Nearest to 梦: 蛟,  ,\n",
      "Nearest to 梦: 蛟,  , 萋,\n",
      "Nearest to 梦: 蛟,  , 萋, 觅,\n",
      "Nearest to 梦: 蛟,  , 萋, 觅, 懊,\n",
      "Nearest to 梦: 蛟,  , 萋, 觅, 懊, 昂,\n",
      "Nearest to 梦: 蛟,  , 萋, 觅, 懊, 昂, 而,\n",
      "Nearest to 梦: 蛟,  , 萋, 觅, 懊, 昂, 而, 澶,\n",
      "[ 830 4673 2049  151 1586 2693    3 2877]\n",
      "Nearest to 中: 赵,\n",
      "Nearest to 中: 赵, 庑,\n",
      "Nearest to 中: 赵, 庑, 卫,\n",
      "Nearest to 中: 赵, 庑, 卫, 语,\n",
      "Nearest to 中: 赵, 庑, 卫, 语, 规,\n",
      "Nearest to 中: 赵, 庑, 卫, 语, 规, 觚,\n",
      "Nearest to 中: 赵, 庑, 卫, 语, 规, 觚, ，,\n",
      "Nearest to 中: 赵, 庑, 卫, 语, 规, 觚, ，, 镫,\n",
      "[3709  666 2768    1 2016    3 3888  714]\n",
      "Nearest to 声: 翚,\n",
      "Nearest to 声: 翚, 村,\n",
      "Nearest to 声: 翚, 村, 踞,\n",
      "Nearest to 声: 翚, 村, 踞, 。,\n",
      "Nearest to 声: 翚, 村, 踞, 。, 靓,\n",
      "Nearest to 声: 翚, 村, 踞, 。, 靓, ，,\n",
      "Nearest to 声: 翚, 村, 踞, 。, 靓, ，, 肇,\n",
      "Nearest to 声: 翚, 村, 踞, 。, 靓, ，, 肇, 扶,\n",
      "Average loss at step  22000 :  5.354521289467812\n",
      "Average loss at step  24000 :  4.9387695444822315\n",
      "Average loss at step  26000 :  4.898199760556221\n",
      "Average loss at step  28000 :  4.86400387609005\n",
      "!!!!!!!!!!data_index!!!!!!!!!!!!!!!!!!!!!!:1903073\n",
      "!!!!!!!!!!span!!!!!!!!!!!!!!!!!!!!!!:3\n",
      "deque([1, 2, 2], maxlen=3)\n",
      "Average loss at step  30000 :  4.575950247526169\n",
      "Average loss at step  32000 :  4.2174273426532745\n",
      "Average loss at step  34000 :  4.236833539366722\n",
      "Average loss at step  36000 :  4.168679655551911\n",
      "Average loss at step  38000 :  4.142268801808357\n",
      "Average loss at step  40000 :  4.137891592681408\n",
      "[  11  104 2041 1375  957 4225 2765 1258]\n",
      "Nearest to 秋: 春,\n",
      "Nearest to 秋: 春, □,\n",
      "Nearest to 秋: 春, □, 旬,\n",
      "Nearest to 秋: 春, □, 旬, 胭,\n",
      "Nearest to 秋: 春, □, 旬, 胭, 阔,\n",
      "Nearest to 秋: 春, □, 旬, 胭, 阔, 芾,\n",
      "Nearest to 秋: 春, □, 旬, 胭, 阔, 芾, 酡,\n",
      "Nearest to 秋: 春, □, 旬, 胭, 阔, 芾, 酡, 牙,\n",
      "[  87   85  104 2405  560 1076 3516  163]\n",
      "Nearest to 为: 与,\n",
      "Nearest to 为: 与, 见,\n",
      "Nearest to 为: 与, 见, □,\n",
      "Nearest to 为: 与, 见, □, 峦,\n",
      "Nearest to 为: 与, 见, □, 峦, 忘,\n",
      "Nearest to 为: 与, 见, □, 峦, 忘, 羡,\n",
      "Nearest to 为: 与, 见, □, 峦, 忘, 羡, 囗,\n",
      "Nearest to 为: 与, 见, □, 峦, 忘, 羡, 囗, 同,\n",
      "[3516 2275 1361   32  961 1148  126  587]\n",
      "Nearest to 行: 囗,\n",
      "Nearest to 行: 囗,  ,\n",
      "Nearest to 行: 囗,  , 芦,\n",
      "Nearest to 行: 囗,  , 芦, 归,\n",
      "Nearest to 行: 囗,  , 芦, 归, 耳,\n",
      "Nearest to 行: 囗,  , 芦, 归, 耳, 妒,\n",
      "Nearest to 行: 囗,  , 芦, 归, 耳, 妒, 平,\n",
      "Nearest to 行: 囗,  , 芦, 归, 耳, 妒, 平, 菩,\n",
      "[ 338 2416  164 3944  216 4329 3516 2125]\n",
      "Nearest to 寒: 余,\n",
      "Nearest to 寒: 余, 矫,\n",
      "Nearest to 寒: 余, 矫, 晚,\n",
      "Nearest to 寒: 余, 矫, 晚, 恃,\n",
      "Nearest to 寒: 余, 矫, 晚, 恃, 凉,\n",
      "Nearest to 寒: 余, 矫, 晚, 恃, 凉, 枳,\n",
      "Nearest to 寒: 余, 矫, 晚, 恃, 凉, 枳, 囗,\n",
      "Nearest to 寒: 余, 矫, 晚, 恃, 凉, 枳, 囗, 嗅,\n",
      "[ 104  774 3529 1139  643 3944  127   64]\n",
      "Nearest to 处: □,\n",
      "Nearest to 处: □, 亲,\n",
      "Nearest to 处: □, 亲, 槿,\n",
      "Nearest to 处: □, 亲, 槿, 鞍,\n",
      "Nearest to 处: □, 亲, 槿, 鞍, 计,\n",
      "Nearest to 处: □, 亲, 槿, 鞍, 计, 恃,\n",
      "Nearest to 处: □, 亲, 槿, 鞍, 计, 恃, 意,\n",
      "Nearest to 处: □, 亲, 槿, 鞍, 计, 恃, 意, 更,\n",
      "[3516  187  501  713 2909  104   96 3317]\n",
      "Nearest to 雨: 囗,\n",
      "Nearest to 雨: 囗, 露,\n",
      "Nearest to 雨: 囗, 露, 角,\n",
      "Nearest to 雨: 囗, 露, 角, 鸟,\n",
      "Nearest to 雨: 囗, 露, 角, 鸟, 堞,\n",
      "Nearest to 雨: 囗, 露, 角, 鸟, 堞, □,\n",
      "Nearest to 雨: 囗, 露, 角, 鸟, 堞, □, 柳,\n",
      "Nearest to 雨: 囗, 露, 角, 鸟, 堞, □, 柳, 荠,\n",
      "[ 106 1782 2479  762 2008 1762  104  440]\n",
      "Nearest to 上: 下,\n",
      "Nearest to 上: 下, 义,\n",
      "Nearest to 上: 下, 义, 襦,\n",
      "Nearest to 上: 下, 义, 襦, 报,\n",
      "Nearest to 上: 下, 义, 襦, 报, 鲛,\n",
      "Nearest to 上: 下, 义, 襦, 报, 鲛, 瓮,\n",
      "Nearest to 上: 下, 义, 襦, 报, 鲛, 瓮, □,\n",
      "Nearest to 上: 下, 义, 襦, 报, 鲛, 瓮, □, 浣,\n",
      "[ 135  134 1680   43  223    4 4483  104]\n",
      "Nearest to 人: 客,\n",
      "Nearest to 人: 客, 我,\n",
      "Nearest to 人: 客, 我, 咫,\n",
      "Nearest to 人: 客, 我, 咫, 谁,\n",
      "Nearest to 人: 客, 我, 咫, 谁, 曾,\n",
      "Nearest to 人: 客, 我, 咫, 谁, 曾, 、,\n",
      "Nearest to 人: 客, 我, 咫, 谁, 曾, 、, 鹈,\n",
      "Nearest to 人: 客, 我, 咫, 谁, 曾, 、, 鹈, □,\n",
      "[   3 3516 1973    4 2170 3712    2  440]\n",
      "Nearest to （: ，,\n",
      "Nearest to （: ，, 囗,\n",
      "Nearest to （: ，, 囗, 昆,\n",
      "Nearest to （: ，, 囗, 昆, 、,\n",
      "Nearest to （: ，, 囗, 昆, 、, 苕,\n",
      "Nearest to （: ，, 囗, 昆, 、, 苕, 沅,\n",
      "Nearest to （: ，, 囗, 昆, 、, 苕, 沅, \n",
      ",\n",
      "Nearest to （: ，, 囗, 昆, 、, 苕, 沅, \n",
      ", 浣,\n",
      "[  69 4116 2003  104 1804 2621 1617 2187]\n",
      "Nearest to 云: 烟,\n",
      "Nearest to 云: 烟, 椎,\n",
      "Nearest to 云: 烟, 椎, 棱,\n",
      "Nearest to 云: 烟, 椎, 棱, □,\n",
      "Nearest to 云: 烟, 椎, 棱, □, 桨,\n",
      "Nearest to 云: 烟, 椎, 棱, □, 桨, 饵,\n",
      "Nearest to 云: 烟, 椎, 棱, □, 桨, 饵, 毛,\n",
      "Nearest to 云: 烟, 椎, 棱, □, 桨, 饵, 毛, 盆,\n",
      "[  28   29  132 3516   87   73    4   97]\n",
      "Nearest to 似: 如,\n",
      "Nearest to 似: 如, 是,\n",
      "Nearest to 似: 如, 是, 到,\n",
      "Nearest to 似: 如, 是, 到, 囗,\n",
      "Nearest to 似: 如, 是, 到, 囗, 与,\n",
      "Nearest to 似: 如, 是, 到, 囗, 与, 为,\n",
      "Nearest to 似: 如, 是, 到, 囗, 与, 为, 、,\n",
      "Nearest to 似: 如, 是, 到, 囗, 与, 为, 、, 多,\n",
      "[  56 3336   71   93   78  490  370    4]\n",
      "Nearest to 东: 西,\n",
      "Nearest to 东: 西, 柝,\n",
      "Nearest to 东: 西, 柝, 南,\n",
      "Nearest to 东: 西, 柝, 南, 流,\n",
      "Nearest to 东: 西, 柝, 南, 流, 前,\n",
      "Nearest to 东: 西, 柝, 南, 流, 前, 汉,\n",
      "Nearest to 东: 西, 柝, 南, 流, 前, 汉, 北,\n",
      "Nearest to 东: 西, 柝, 南, 流, 前, 汉, 北, 、,\n",
      "[ 133 3516   59  741  406 4141 1573 3142]\n",
      "Nearest to 愁: 恨,\n",
      "Nearest to 愁: 恨, 囗,\n",
      "Nearest to 愁: 恨, 囗, 情,\n",
      "Nearest to 愁: 恨, 囗, 情, 扫,\n",
      "Nearest to 愁: 恨, 囗, 情, 扫, 怕,\n",
      "Nearest to 愁: 恨, 囗, 情, 扫, 怕, 戮,\n",
      "Nearest to 愁: 恨, 囗, 情, 扫, 怕, 戮, 惨,\n",
      "Nearest to 愁: 恨, 囗, 情, 扫, 怕, 戮, 惨, 演,\n",
      "[2162 1790 5177 2275 1584  104 2525  408]\n",
      "Nearest to 梦: 蛟,\n",
      "Nearest to 梦: 蛟, 萋,\n",
      "Nearest to 梦: 蛟, 萋, 徯,\n",
      "Nearest to 梦: 蛟, 萋, 徯,  ,\n",
      "Nearest to 梦: 蛟, 萋, 徯,  , 区,\n",
      "Nearest to 梦: 蛟, 萋, 徯,  , 区, □,\n",
      "Nearest to 梦: 蛟, 萋, 徯,  , 区, □, 昂,\n",
      "Nearest to 梦: 蛟, 萋, 徯,  , 区, □, 昂, 而,\n",
      "[3516  830 4673  293 3146 2877  159 2222]\n",
      "Nearest to 中: 囗,\n",
      "Nearest to 中: 囗, 赵,\n",
      "Nearest to 中: 囗, 赵, 庑,\n",
      "Nearest to 中: 囗, 赵, 庑, 赋,\n",
      "Nearest to 中: 囗, 赵, 庑, 赋, 撒,\n",
      "Nearest to 中: 囗, 赵, 庑, 赋, 撒, 镫,\n",
      "Nearest to 中: 囗, 赵, 庑, 赋, 撒, 镫, 后,\n",
      "Nearest to 中: 囗, 赵, 庑, 赋, 撒, 镫, 后, 隙,\n",
      "[2673 3709 2016  151 3179 3888 2768  203]\n",
      "Nearest to 声: 剔,\n",
      "Nearest to 声: 剔, 翚,\n",
      "Nearest to 声: 剔, 翚, 靓,\n",
      "Nearest to 声: 剔, 翚, 靓, 语,\n",
      "Nearest to 声: 剔, 翚, 靓, 语, 黔,\n",
      "Nearest to 声: 剔, 翚, 靓, 语, 黔, 肇,\n",
      "Nearest to 声: 剔, 翚, 靓, 语, 黔, 肇, 踞,\n",
      "Nearest to 声: 剔, 翚, 靓, 语, 黔, 肇, 踞, 调,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  42000 :  4.114939044594765\n",
      "Average loss at step  44000 :  4.10662106692791\n",
      "Average loss at step  46000 :  4.123880360484123\n",
      "Average loss at step  48000 :  4.174548446536064\n",
      "Average loss at step  50000 :  4.135100463747978\n",
      "Average loss at step  52000 :  4.141639880537987\n",
      "Average loss at step  54000 :  4.077484606862068\n",
      "Average loss at step  56000 :  4.12939732170105\n",
      "Average loss at step  58000 :  4.157644516587258\n",
      "!!!!!!!!!!data_index!!!!!!!!!!!!!!!!!!!!!!:1903073\n",
      "!!!!!!!!!!span!!!!!!!!!!!!!!!!!!!!!!:3\n",
      "deque([1, 2, 2], maxlen=3)\n",
      "Average loss at step  60000 :  4.058831264734268\n",
      "[  11 1375   53    4  402 1212 2765  957]\n",
      "Nearest to 秋: 春,\n",
      "Nearest to 秋: 春, 胭,\n",
      "Nearest to 秋: 春, 胭, 寒,\n",
      "Nearest to 秋: 春, 胭, 寒, 、,\n",
      "Nearest to 秋: 春, 胭, 寒, 、, 渐,\n",
      "Nearest to 秋: 春, 胭, 寒, 、, 渐, 腊,\n",
      "Nearest to 秋: 春, 胭, 寒, 、, 渐, 腊, 酡,\n",
      "Nearest to 秋: 春, 胭, 寒, 、, 渐, 腊, 酡, 阔,\n",
      "[  87   19   85   86  163  454 2405 1410]\n",
      "Nearest to 为: 与,\n",
      "Nearest to 为: 与, 有,\n",
      "Nearest to 为: 与, 有, 见,\n",
      "Nearest to 为: 与, 有, 见, 似,\n",
      "Nearest to 为: 与, 有, 见, 似, 同,\n",
      "Nearest to 为: 与, 有, 见, 似, 同, 以,\n",
      "Nearest to 为: 与, 有, 见, 似, 同, 以, 峦,\n",
      "Nearest to 为: 与, 有, 见, 似, 同, 以, 峦, 枯,\n",
      "[3516   32 1361  961 1148  920 2275    1]\n",
      "Nearest to 行: 囗,\n",
      "Nearest to 行: 囗, 归,\n",
      "Nearest to 行: 囗, 归, 芦,\n",
      "Nearest to 行: 囗, 归, 芦, 耳,\n",
      "Nearest to 行: 囗, 归, 芦, 耳, 妒,\n",
      "Nearest to 行: 囗, 归, 芦, 耳, 妒, 诸,\n",
      "Nearest to 行: 囗, 归, 芦, 耳, 妒, 诸,  ,\n",
      "Nearest to 行: 囗, 归, 芦, 耳, 妒, 诸,  , 。,\n",
      "[ 338  216  164 2416   44 4329    7  241]\n",
      "Nearest to 寒: 余,\n",
      "Nearest to 寒: 余, 凉,\n",
      "Nearest to 寒: 余, 凉, 晚,\n",
      "Nearest to 寒: 余, 凉, 晚, 矫,\n",
      "Nearest to 寒: 余, 凉, 晚, 矫, 秋,\n",
      "Nearest to 寒: 余, 凉, 晚, 矫, 秋, 枳,\n",
      "Nearest to 寒: 余, 凉, 晚, 矫, 秋, 枳, 风,\n",
      "Nearest to 寒: 余, 凉, 晚, 矫, 秋, 枳, 风, 冷,\n",
      "[ 774  104  127 3529   64  813    5 1139]\n",
      "Nearest to 处: 亲,\n",
      "Nearest to 处: 亲, □,\n",
      "Nearest to 处: 亲, □, 意,\n",
      "Nearest to 处: 亲, □, 意, 槿,\n",
      "Nearest to 处: 亲, □, 意, 槿, 更,\n",
      "Nearest to 处: 亲, □, 意, 槿, 更, 妨,\n",
      "Nearest to 处: 亲, □, 意, 槿, 更, 妨, 人,\n",
      "Nearest to 处: 亲, □, 意, 槿, 更, 妨, 人, 鞍,\n",
      "[3516  187   96    2 2909 3317  501 1145]\n",
      "Nearest to 雨: 囗,\n",
      "Nearest to 雨: 囗, 露,\n",
      "Nearest to 雨: 囗, 露, 柳,\n",
      "Nearest to 雨: 囗, 露, 柳, \n",
      ",\n",
      "Nearest to 雨: 囗, 露, 柳, \n",
      ", 堞,\n",
      "Nearest to 雨: 囗, 露, 柳, \n",
      ", 堞, 荠,\n",
      "Nearest to 雨: 囗, 露, 柳, \n",
      ", 堞, 荠, 角,\n",
      "Nearest to 雨: 囗, 露, 柳, \n",
      ", 堞, 荠, 角, 涛,\n",
      "[ 106  210 2479  174    3    1 2008 1782]\n",
      "Nearest to 上: 下,\n",
      "Nearest to 上: 下, 边,\n",
      "Nearest to 上: 下, 边, 襦,\n",
      "Nearest to 上: 下, 边, 襦, 外,\n",
      "Nearest to 上: 下, 边, 襦, 外, ，,\n",
      "Nearest to 上: 下, 边, 襦, 外, ，, 。,\n",
      "Nearest to 上: 下, 边, 襦, 外, ，, 。, 鲛,\n",
      "Nearest to 上: 下, 边, 襦, 外, ，, 。, 鲛, 义,\n",
      "[   1  135 1680    3  449    4  134 2674]\n",
      "Nearest to 人: 。,\n",
      "Nearest to 人: 。, 客,\n",
      "Nearest to 人: 。, 客, 咫,\n",
      "Nearest to 人: 。, 客, 咫, ，,\n",
      "Nearest to 人: 。, 客, 咫, ，, 屏,\n",
      "Nearest to 人: 。, 客, 咫, ，, 屏, 、,\n",
      "Nearest to 人: 。, 客, 咫, ，, 屏, 、, 我,\n",
      "Nearest to 人: 。, 客, 咫, ，, 屏, 、, 我, 航,\n",
      "[   3    4 3516   10  831 1973 1053 2170]\n",
      "Nearest to （: ，,\n",
      "Nearest to （: ，, 、,\n",
      "Nearest to （: ，, 、, 囗,\n",
      "Nearest to （: ，, 、, 囗, ）,\n",
      "Nearest to （: ，, 、, 囗, ）, ·,\n",
      "Nearest to （: ，, 、, 囗, ）, ·, 昆,\n",
      "Nearest to （: ，, 、, 囗, ）, ·, 昆, 殷,\n",
      "Nearest to （: ，, 、, 囗, ）, ·, 昆, 殷, 苕,\n",
      "[  69 4116 2003    1   13 2621    3    4]\n",
      "Nearest to 云: 烟,\n",
      "Nearest to 云: 烟, 椎,\n",
      "Nearest to 云: 烟, 椎, 棱,\n",
      "Nearest to 云: 烟, 椎, 棱, 。,\n",
      "Nearest to 云: 烟, 椎, 棱, 。, 天,\n",
      "Nearest to 云: 烟, 椎, 棱, 。, 天, 饵,\n",
      "Nearest to 云: 烟, 椎, 棱, 。, 天, 饵, ，,\n",
      "Nearest to 云: 烟, 椎, 棱, 。, 天, 饵, ，, 、,\n",
      "[  28   29  132   73 3516   87    4  693]\n",
      "Nearest to 似: 如,\n",
      "Nearest to 似: 如, 是,\n",
      "Nearest to 似: 如, 是, 到,\n",
      "Nearest to 似: 如, 是, 到, 为,\n",
      "Nearest to 似: 如, 是, 到, 为, 囗,\n",
      "Nearest to 似: 如, 是, 到, 为, 囗, 与,\n",
      "Nearest to 似: 如, 是, 到, 为, 囗, 与, 、,\n",
      "Nearest to 似: 如, 是, 到, 为, 囗, 与, 、, 亦,\n",
      "[  56   71  370  809 3336   93    4  490]\n",
      "Nearest to 东: 西,\n",
      "Nearest to 东: 西, 南,\n",
      "Nearest to 东: 西, 南, 北,\n",
      "Nearest to 东: 西, 南, 北, 薰,\n",
      "Nearest to 东: 西, 南, 北, 薰, 柝,\n",
      "Nearest to 东: 西, 南, 北, 薰, 柝, 流,\n",
      "Nearest to 东: 西, 南, 北, 薰, 柝, 流, 、,\n",
      "Nearest to 东: 西, 南, 北, 薰, 柝, 流, 、, 汉,\n",
      "[ 133   59 3516  741  406    4 3611 3142]\n",
      "Nearest to 愁: 恨,\n",
      "Nearest to 愁: 恨, 情,\n",
      "Nearest to 愁: 恨, 情, 囗,\n",
      "Nearest to 愁: 恨, 情, 囗, 扫,\n",
      "Nearest to 愁: 恨, 情, 囗, 扫, 怕,\n",
      "Nearest to 愁: 恨, 情, 囗, 扫, 怕, 、,\n",
      "Nearest to 愁: 恨, 情, 囗, 扫, 怕, 、, 侠,\n",
      "Nearest to 愁: 恨, 情, 囗, 扫, 怕, 、, 侠, 演,\n",
      "[2162 5177 1584   38 1790 2525  408   50]\n",
      "Nearest to 梦: 蛟,\n",
      "Nearest to 梦: 蛟, 徯,\n",
      "Nearest to 梦: 蛟, 徯, 区,\n",
      "Nearest to 梦: 蛟, 徯, 区, 去,\n",
      "Nearest to 梦: 蛟, 徯, 区, 去, 萋,\n",
      "Nearest to 梦: 蛟, 徯, 区, 去, 萋, 昂,\n",
      "Nearest to 梦: 蛟, 徯, 区, 去, 萋, 昂, 而,\n",
      "Nearest to 梦: 蛟, 徯, 区, 去, 萋, 昂, 而, 夜,\n",
      "[3516 4673  830   46  293 3146 2222 2824]\n",
      "Nearest to 中: 囗,\n",
      "Nearest to 中: 囗, 庑,\n",
      "Nearest to 中: 囗, 庑, 赵,\n",
      "Nearest to 中: 囗, 庑, 赵, 里,\n",
      "Nearest to 中: 囗, 庑, 赵, 里, 赋,\n",
      "Nearest to 中: 囗, 庑, 赵, 里, 赋, 撒,\n",
      "Nearest to 中: 囗, 庑, 赵, 里, 赋, 撒, 隙,\n",
      "Nearest to 中: 囗, 庑, 赵, 里, 赋, 撒, 隙, 决,\n",
      "[ 151 2673 2016 2564  637 3179 1652 3709]\n",
      "Nearest to 声: 语,\n",
      "Nearest to 声: 语, 剔,\n",
      "Nearest to 声: 语, 剔, 靓,\n",
      "Nearest to 声: 语, 剔, 靓, 庸,\n",
      "Nearest to 声: 语, 剔, 靓, 庸, 鸣,\n",
      "Nearest to 声: 语, 剔, 靓, 庸, 鸣, 黔,\n",
      "Nearest to 声: 语, 剔, 靓, 庸, 鸣, 黔, 骤,\n",
      "Nearest to 声: 语, 剔, 靓, 庸, 鸣, 黔, 骤, 翚,\n",
      "Average loss at step  62000 :  3.963340236067772\n",
      "Average loss at step  64000 :  3.9774837460517882\n",
      "Average loss at step  66000 :  3.996181500315666\n",
      "Average loss at step  68000 :  3.9739535710811613\n",
      "Average loss at step  70000 :  3.9775356429219246\n",
      "Average loss at step  72000 :  3.9849442050457\n",
      "Average loss at step  74000 :  3.9867947895526887\n",
      "Average loss at step  76000 :  4.048902478337288\n",
      "Average loss at step  78000 :  4.057757090091705\n",
      "Average loss at step  80000 :  4.053626470208168\n",
      "[  11   53    4   14 1212    3  402 2765]\n",
      "Nearest to 秋: 春,\n",
      "Nearest to 秋: 春, 寒,\n",
      "Nearest to 秋: 春, 寒, 、,\n",
      "Nearest to 秋: 春, 寒, 、, 月,\n",
      "Nearest to 秋: 春, 寒, 、, 月, 腊,\n",
      "Nearest to 秋: 春, 寒, 、, 月, 腊, ，,\n",
      "Nearest to 秋: 春, 寒, 、, 月, 腊, ，, 渐,\n",
      "Nearest to 秋: 春, 寒, 、, 月, 腊, ，, 渐, 酡,\n",
      "[  87  454   19   85   86    3  163 3516]\n",
      "Nearest to 为: 与,\n",
      "Nearest to 为: 与, 以,\n",
      "Nearest to 为: 与, 以, 有,\n",
      "Nearest to 为: 与, 以, 有, 见,\n",
      "Nearest to 为: 与, 以, 有, 见, 似,\n",
      "Nearest to 为: 与, 以, 有, 见, 似, ，,\n",
      "Nearest to 为: 与, 以, 有, 见, 似, ，, 同,\n",
      "Nearest to 为: 与, 以, 有, 见, 似, ，, 同, 囗,\n",
      "[3516 1361   32  920  961  587 1148  126]\n",
      "Nearest to 行: 囗,\n",
      "Nearest to 行: 囗, 芦,\n",
      "Nearest to 行: 囗, 芦, 归,\n",
      "Nearest to 行: 囗, 芦, 归, 诸,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳, 菩,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳, 菩, 妒,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳, 菩, 妒, 平,\n",
      "[ 216  338  164   44 2416    7  241  283]\n",
      "Nearest to 寒: 凉,\n",
      "Nearest to 寒: 凉, 余,\n",
      "Nearest to 寒: 凉, 余, 晚,\n",
      "Nearest to 寒: 凉, 余, 晚, 秋,\n",
      "Nearest to 寒: 凉, 余, 晚, 秋, 矫,\n",
      "Nearest to 寒: 凉, 余, 晚, 秋, 矫, 风,\n",
      "Nearest to 寒: 凉, 余, 晚, 秋, 矫, 风, 冷,\n",
      "Nearest to 寒: 凉, 余, 晚, 秋, 矫, 风, 冷, 晴,\n",
      "[ 127  104  774   64 3529  813   21  204]\n",
      "Nearest to 处: 意,\n",
      "Nearest to 处: 意, □,\n",
      "Nearest to 处: 意, □, 亲,\n",
      "Nearest to 处: 意, □, 亲, 更,\n",
      "Nearest to 处: 意, □, 亲, 更, 槿,\n",
      "Nearest to 处: 意, □, 亲, 更, 槿, 妨,\n",
      "Nearest to 处: 意, □, 亲, 更, 槿, 妨, 时,\n",
      "Nearest to 处: 意, □, 亲, 更, 槿, 妨, 时, 地,\n",
      "[3516  187   96 3529 4299 2909  501 2836]\n",
      "Nearest to 雨: 囗,\n",
      "Nearest to 雨: 囗, 露,\n",
      "Nearest to 雨: 囗, 露, 柳,\n",
      "Nearest to 雨: 囗, 露, 柳, 槿,\n",
      "Nearest to 雨: 囗, 露, 柳, 槿, 谶,\n",
      "Nearest to 雨: 囗, 露, 柳, 槿, 谶, 堞,\n",
      "Nearest to 雨: 囗, 露, 柳, 槿, 谶, 堞, 角,\n",
      "Nearest to 雨: 囗, 露, 柳, 槿, 谶, 堞, 角, 舻,\n",
      "[ 106  210  174  144 1762   80  762  132]\n",
      "Nearest to 上: 下,\n",
      "Nearest to 上: 下, 边,\n",
      "Nearest to 上: 下, 边, 外,\n",
      "Nearest to 上: 下, 边, 外, 作,\n",
      "Nearest to 上: 下, 边, 外, 作, 瓮,\n",
      "Nearest to 上: 下, 边, 外, 作, 瓮, 头,\n",
      "Nearest to 上: 下, 边, 外, 作, 瓮, 头, 报,\n",
      "Nearest to 上: 下, 边, 外, 作, 瓮, 头, 报, 到,\n",
      "[ 135 1680    3   11    1    4  134  449]\n",
      "Nearest to 人: 客,\n",
      "Nearest to 人: 客, 咫,\n",
      "Nearest to 人: 客, 咫, ，,\n",
      "Nearest to 人: 客, 咫, ，, 春,\n",
      "Nearest to 人: 客, 咫, ，, 春, 。,\n",
      "Nearest to 人: 客, 咫, ，, 春, 。, 、,\n",
      "Nearest to 人: 客, 咫, ，, 春, 。, 、, 我,\n",
      "Nearest to 人: 客, 咫, ，, 春, 。, 、, 我, 屏,\n",
      "[   3    4 3516 2074 1053 1973 1701  594]\n",
      "Nearest to （: ，,\n",
      "Nearest to （: ，, 、,\n",
      "Nearest to （: ，, 、, 囗,\n",
      "Nearest to （: ，, 、, 囗, 倅,\n",
      "Nearest to （: ，, 、, 囗, 倅, 殷,\n",
      "Nearest to （: ，, 、, 囗, 倅, 殷, 昆,\n",
      "Nearest to （: ，, 、, 囗, 倅, 殷, 昆, 乔,\n",
      "Nearest to （: ，, 、, 囗, 倅, 殷, 昆, 乔, 萨,\n",
      "[  69 2003 4116    4  323 2621 1804 3095]\n",
      "Nearest to 云: 烟,\n",
      "Nearest to 云: 烟, 棱,\n",
      "Nearest to 云: 烟, 棱, 椎,\n",
      "Nearest to 云: 烟, 棱, 椎, 、,\n",
      "Nearest to 云: 烟, 棱, 椎, 、, 卷,\n",
      "Nearest to 云: 烟, 棱, 椎, 、, 卷, 饵,\n",
      "Nearest to 云: 烟, 棱, 椎, 、, 卷, 饵, 桨,\n",
      "Nearest to 云: 烟, 棱, 椎, 、, 卷, 饵, 桨, 狗,\n",
      "[  28   29    4   87  132  332   73 3516]\n",
      "Nearest to 似: 如,\n",
      "Nearest to 似: 如, 是,\n",
      "Nearest to 似: 如, 是, 、,\n",
      "Nearest to 似: 如, 是, 、, 与,\n",
      "Nearest to 似: 如, 是, 、, 与, 到,\n",
      "Nearest to 似: 如, 是, 、, 与, 到, 出,\n",
      "Nearest to 似: 如, 是, 、, 与, 到, 出, 为,\n",
      "Nearest to 似: 如, 是, 、, 与, 到, 出, 为, 囗,\n",
      "[  56  370   71  809   93 3336    4    3]\n",
      "Nearest to 东: 西,\n",
      "Nearest to 东: 西, 北,\n",
      "Nearest to 东: 西, 北, 南,\n",
      "Nearest to 东: 西, 北, 南, 薰,\n",
      "Nearest to 东: 西, 北, 南, 薰, 流,\n",
      "Nearest to 东: 西, 北, 南, 薰, 流, 柝,\n",
      "Nearest to 东: 西, 北, 南, 薰, 流, 柝, 、,\n",
      "Nearest to 东: 西, 北, 南, 薰, 流, 柝, 、, ，,\n",
      "[ 133   59 3516  406  741 3611    4 3142]\n",
      "Nearest to 愁: 恨,\n",
      "Nearest to 愁: 恨, 情,\n",
      "Nearest to 愁: 恨, 情, 囗,\n",
      "Nearest to 愁: 恨, 情, 囗, 怕,\n",
      "Nearest to 愁: 恨, 情, 囗, 怕, 扫,\n",
      "Nearest to 愁: 恨, 情, 囗, 怕, 扫, 侠,\n",
      "Nearest to 愁: 恨, 情, 囗, 怕, 扫, 侠, 、,\n",
      "Nearest to 愁: 恨, 情, 囗, 怕, 扫, 侠, 、, 演,\n",
      "[1584 5177   38 2162 2753   50 1790  133]\n",
      "Nearest to 梦: 区,\n",
      "Nearest to 梦: 区, 徯,\n",
      "Nearest to 梦: 区, 徯, 去,\n",
      "Nearest to 梦: 区, 徯, 去, 蛟,\n",
      "Nearest to 梦: 区, 徯, 去, 蛟, 飏,\n",
      "Nearest to 梦: 区, 徯, 去, 蛟, 飏, 夜,\n",
      "Nearest to 梦: 区, 徯, 去, 蛟, 飏, 夜, 萋,\n",
      "Nearest to 梦: 区, 徯, 去, 蛟, 飏, 夜, 萋, 恨,\n",
      "[  46 3516 4673  293    3 2824  830   77]\n",
      "Nearest to 中: 里,\n",
      "Nearest to 中: 里, 囗,\n",
      "Nearest to 中: 里, 囗, 庑,\n",
      "Nearest to 中: 里, 囗, 庑, 赋,\n",
      "Nearest to 中: 里, 囗, 庑, 赋, ，,\n",
      "Nearest to 中: 里, 囗, 庑, 赋, ，, 决,\n",
      "Nearest to 中: 里, 囗, 庑, 赋, ，, 决, 赵,\n",
      "Nearest to 中: 里, 囗, 庑, 赋, ，, 决, 赵, 空,\n",
      "[ 151  637 2564 2016 3179 2673 1652 2768]\n",
      "Nearest to 声: 语,\n",
      "Nearest to 声: 语, 鸣,\n",
      "Nearest to 声: 语, 鸣, 庸,\n",
      "Nearest to 声: 语, 鸣, 庸, 靓,\n",
      "Nearest to 声: 语, 鸣, 庸, 靓, 黔,\n",
      "Nearest to 声: 语, 鸣, 庸, 靓, 黔, 剔,\n",
      "Nearest to 声: 语, 鸣, 庸, 靓, 黔, 剔, 骤,\n",
      "Nearest to 声: 语, 鸣, 庸, 靓, 黔, 剔, 骤, 踞,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  82000 :  4.028093996047974\n",
      "Average loss at step  84000 :  4.004042298913002\n",
      "Average loss at step  86000 :  4.054868120908737\n",
      "Average loss at step  88000 :  4.041585693478584\n",
      "!!!!!!!!!!data_index!!!!!!!!!!!!!!!!!!!!!!:1903073\n",
      "!!!!!!!!!!span!!!!!!!!!!!!!!!!!!!!!!:3\n",
      "deque([1, 2, 2], maxlen=3)\n",
      "Average loss at step  90000 :  3.9581678332090378\n",
      "Average loss at step  92000 :  3.9084132342338562\n",
      "Average loss at step  94000 :  3.936595415353775\n",
      "Average loss at step  96000 :  3.907990253806114\n",
      "Average loss at step  98000 :  3.934517460346222\n",
      "Average loss at step  100000 :  3.917259005010128\n",
      "[  11   53   14 1022 1212  957  198 3429]\n",
      "Nearest to 秋: 春,\n",
      "Nearest to 秋: 春, 寒,\n",
      "Nearest to 秋: 春, 寒, 月,\n",
      "Nearest to 秋: 春, 寒, 月, 韶,\n",
      "Nearest to 秋: 春, 寒, 月, 韶, 腊,\n",
      "Nearest to 秋: 春, 寒, 月, 韶, 腊, 阔,\n",
      "Nearest to 秋: 春, 寒, 月, 韶, 腊, 阔, 晓,\n",
      "Nearest to 秋: 春, 寒, 月, 韶, 腊, 阔, 晓, 谦,\n",
      "[  87  454   86   19  163  205   85 2405]\n",
      "Nearest to 为: 与,\n",
      "Nearest to 为: 与, 以,\n",
      "Nearest to 为: 与, 以, 似,\n",
      "Nearest to 为: 与, 以, 似, 有,\n",
      "Nearest to 为: 与, 以, 似, 有, 同,\n",
      "Nearest to 为: 与, 以, 似, 有, 同, 入,\n",
      "Nearest to 为: 与, 以, 似, 有, 同, 入, 见,\n",
      "Nearest to 为: 与, 以, 似, 有, 同, 入, 见, 峦,\n",
      "[3516 1361   32  920  961  587  126 1554]\n",
      "Nearest to 行: 囗,\n",
      "Nearest to 行: 囗, 芦,\n",
      "Nearest to 行: 囗, 芦, 归,\n",
      "Nearest to 行: 囗, 芦, 归, 诸,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳, 菩,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳, 菩, 平,\n",
      "Nearest to 行: 囗, 芦, 归, 诸, 耳, 菩, 平, 斯,\n",
      "[ 216  338   44  164  347  241 2416  283]\n",
      "Nearest to 寒: 凉,\n",
      "Nearest to 寒: 凉, 余,\n",
      "Nearest to 寒: 凉, 余, 秋,\n",
      "Nearest to 寒: 凉, 余, 秋, 晚,\n",
      "Nearest to 寒: 凉, 余, 秋, 晚, 暖,\n",
      "Nearest to 寒: 凉, 余, 秋, 晚, 暖, 冷,\n",
      "Nearest to 寒: 凉, 余, 秋, 晚, 暖, 冷, 矫,\n",
      "Nearest to 寒: 凉, 余, 秋, 晚, 暖, 冷, 矫, 晴,\n",
      "[ 104   21   64  813  127  774  683 1139]\n",
      "Nearest to 处: □,\n",
      "Nearest to 处: □, 时,\n",
      "Nearest to 处: □, 时, 更,\n",
      "Nearest to 处: □, 时, 更, 妨,\n",
      "Nearest to 处: □, 时, 更, 妨, 意,\n",
      "Nearest to 处: □, 时, 更, 妨, 意, 亲,\n",
      "Nearest to 处: □, 时, 更, 妨, 意, 亲, 限,\n",
      "Nearest to 处: □, 时, 更, 妨, 意, 亲, 限, 鞍,\n",
      "[3516   96  104  187 4299 2909 2678  268]\n",
      "Nearest to 雨: 囗,\n",
      "Nearest to 雨: 囗, 柳,\n",
      "Nearest to 雨: 囗, 柳, □,\n",
      "Nearest to 雨: 囗, 柳, □, 露,\n",
      "Nearest to 雨: 囗, 柳, □, 露, 谶,\n",
      "Nearest to 雨: 囗, 柳, □, 露, 谶, 堞,\n",
      "Nearest to 雨: 囗, 柳, □, 露, 谶, 堞, 忡,\n",
      "Nearest to 雨: 囗, 柳, □, 露, 谶, 堞, 忡, 听,\n",
      "[ 106  210 1762  144  174   80 2479 1665]\n",
      "Nearest to 上: 下,\n",
      "Nearest to 上: 下, 边,\n",
      "Nearest to 上: 下, 边, 瓮,\n",
      "Nearest to 上: 下, 边, 瓮, 作,\n",
      "Nearest to 上: 下, 边, 瓮, 作, 外,\n",
      "Nearest to 上: 下, 边, 瓮, 作, 外, 头,\n",
      "Nearest to 上: 下, 边, 瓮, 作, 外, 头, 襦,\n",
      "Nearest to 上: 下, 边, 瓮, 作, 外, 头, 襦, 庵,\n",
      "[ 135 1680  134    4   59    1   43  320]\n",
      "Nearest to 人: 客,\n",
      "Nearest to 人: 客, 咫,\n",
      "Nearest to 人: 客, 咫, 我,\n",
      "Nearest to 人: 客, 咫, 我, 、,\n",
      "Nearest to 人: 客, 咫, 我, 、, 情,\n",
      "Nearest to 人: 客, 咫, 我, 、, 情, 。,\n",
      "Nearest to 人: 客, 咫, 我, 、, 情, 。, 谁,\n",
      "Nearest to 人: 客, 咫, 我, 、, 情, 。, 谁, 著,\n",
      "[   3    4    2 3516  831  594 1973 2690]\n",
      "Nearest to （: ，,\n",
      "Nearest to （: ，, 、,\n",
      "Nearest to （: ，, 、, \n",
      ",\n",
      "Nearest to （: ，, 、, \n",
      ", 囗,\n",
      "Nearest to （: ，, 、, \n",
      ", 囗, ·,\n",
      "Nearest to （: ，, 、, \n",
      ", 囗, ·, 萨,\n",
      "Nearest to （: ，, 、, \n",
      ", 囗, ·, 萨, 昆,\n",
      "Nearest to （: ，, 、, \n",
      ", 囗, ·, 萨, 昆, 邑,\n",
      "[  69 2003  104 4116 4106  323 3095  757]\n",
      "Nearest to 云: 烟,\n",
      "Nearest to 云: 烟, 棱,\n",
      "Nearest to 云: 烟, 棱, □,\n",
      "Nearest to 云: 烟, 棱, □, 椎,\n",
      "Nearest to 云: 烟, 棱, □, 椎, 楯,\n",
      "Nearest to 云: 烟, 棱, □, 椎, 楯, 卷,\n",
      "Nearest to 云: 烟, 棱, □, 椎, 楯, 卷, 狗,\n",
      "Nearest to 云: 烟, 棱, □, 椎, 楯, 卷, 狗, 帆,\n",
      "[ 28  29  87  73 693   4 132 101]\n",
      "Nearest to 似: 如,\n",
      "Nearest to 似: 如, 是,\n",
      "Nearest to 似: 如, 是, 与,\n",
      "Nearest to 似: 如, 是, 与, 为,\n",
      "Nearest to 似: 如, 是, 与, 为, 亦,\n",
      "Nearest to 似: 如, 是, 与, 为, 亦, 、,\n",
      "Nearest to 似: 如, 是, 与, 为, 亦, 、, 到,\n",
      "Nearest to 似: 如, 是, 与, 为, 亦, 、, 到, 在,\n",
      "[  56  370   71  809    4  228  903 1716]\n",
      "Nearest to 东: 西,\n",
      "Nearest to 东: 西, 北,\n",
      "Nearest to 东: 西, 北, 南,\n",
      "Nearest to 东: 西, 北, 南, 薰,\n",
      "Nearest to 东: 西, 北, 南, 薰, 、,\n",
      "Nearest to 东: 西, 北, 南, 薰, 、, 斜,\n",
      "Nearest to 东: 西, 北, 南, 薰, 、, 斜, 苏,\n",
      "Nearest to 东: 西, 北, 南, 薰, 、, 斜, 苏, 渭,\n",
      "[ 133   59  406 3516  741 2878 3611    1]\n",
      "Nearest to 愁: 恨,\n",
      "Nearest to 愁: 恨, 情,\n",
      "Nearest to 愁: 恨, 情, 怕,\n",
      "Nearest to 愁: 恨, 情, 怕, 囗,\n",
      "Nearest to 愁: 恨, 情, 怕, 囗, 扫,\n",
      "Nearest to 愁: 恨, 情, 怕, 囗, 扫, 蹉,\n",
      "Nearest to 愁: 恨, 情, 怕, 囗, 扫, 蹉, 侠,\n",
      "Nearest to 愁: 恨, 情, 怕, 囗, 扫, 蹉, 侠, 。,\n",
      "[1584   38 5177   50 2162  133  104 2753]\n",
      "Nearest to 梦: 区,\n",
      "Nearest to 梦: 区, 去,\n",
      "Nearest to 梦: 区, 去, 徯,\n",
      "Nearest to 梦: 区, 去, 徯, 夜,\n",
      "Nearest to 梦: 区, 去, 徯, 夜, 蛟,\n",
      "Nearest to 梦: 区, 去, 徯, 夜, 蛟, 恨,\n",
      "Nearest to 梦: 区, 去, 徯, 夜, 蛟, 恨, □,\n",
      "Nearest to 梦: 区, 去, 徯, 夜, 蛟, 恨, □, 飏,\n",
      "[  46 3516  144  293 2824 4673   77 3146]\n",
      "Nearest to 中: 里,\n",
      "Nearest to 中: 里, 囗,\n",
      "Nearest to 中: 里, 囗, 作,\n",
      "Nearest to 中: 里, 囗, 作, 赋,\n",
      "Nearest to 中: 里, 囗, 作, 赋, 决,\n",
      "Nearest to 中: 里, 囗, 作, 赋, 决, 庑,\n",
      "Nearest to 中: 里, 囗, 作, 赋, 决, 庑, 空,\n",
      "Nearest to 中: 里, 囗, 作, 赋, 决, 庑, 空, 撒,\n",
      "[ 151 2564  637  438 2016 3179 2673  203]\n",
      "Nearest to 声: 语,\n",
      "Nearest to 声: 语, 庸,\n",
      "Nearest to 声: 语, 庸, 鸣,\n",
      "Nearest to 声: 语, 庸, 鸣, 闻,\n",
      "Nearest to 声: 语, 庸, 鸣, 闻, 靓,\n",
      "Nearest to 声: 语, 庸, 鸣, 闻, 靓, 黔,\n",
      "Nearest to 声: 语, 庸, 鸣, 闻, 靓, 黔, 剔,\n",
      "Nearest to 声: 语, 庸, 鸣, 闻, 靓, 黔, 剔, 调,\n"
     ]
    }
   ],
   "source": [
    "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# ==============================================================================\n",
    "\"\"\"Basic word2vec example.\"\"\"\n",
    "\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import collections\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "from tempfile import gettempdir\n",
    "import zipfile\n",
    "import json\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "# Step 1: Download the data.\n",
    "#url = 'http://mattmahoney.net/dc/'\n",
    "#plt.rcParams['font.sans-serif']=['SimHei']\n",
    "\n",
    "\n",
    "# pylint: disable=redefined-outer-name\n",
    "def maybe_download(filename, expected_bytes):\n",
    "  \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
    "  local_filename = os.path.join(gettempdir(), filename)\n",
    "  if not os.path.exists(local_filename):\n",
    "    local_filename, _ = urllib.request.urlretrieve(url + filename,\n",
    "                                                   local_filename)\n",
    "  statinfo = os.stat(local_filename)\n",
    "  if statinfo.st_size == expected_bytes:\n",
    "    print('Found and verified', filename)\n",
    "  else:\n",
    "    print(statinfo.st_size)\n",
    "    raise Exception('Failed to verify ' + local_filename +\n",
    "                    '. Can you get to it with a browser?')\n",
    "  return local_filename\n",
    "\n",
    "\n",
    "#filename = maybe_download('text8.zip', 31344016)\n",
    "\n",
    "\n",
    "# Read the data into a list of strings.\n",
    "def read_data(filename):\n",
    "  \"\"\"Extract the first file enclosed in a zip file as a list of words.\"\"\"\n",
    "  with open(filename) as f:\n",
    "    data = f.read()\n",
    "  data = [word for word in data]\n",
    "  return data\n",
    "\n",
    "vocabulary = read_data(\"QuanSongCi.txt\")\n",
    "print('Data size', len(vocabulary))\n",
    "\n",
    "# Step 2: Build the dictionary and replace rare words with UNK token.\n",
    "vocabulary_size = 50000\n",
    "\n",
    "\n",
    "def build_dataset(words, n_words):\n",
    "  \"\"\"Process raw inputs into a dataset.\"\"\"\n",
    "  count = [['UNK', -1]]\n",
    "  count.extend(collections.Counter(words).most_common(n_words - 1))\n",
    "  dictionary = dict()\n",
    "  for word, _ in count:\n",
    "    dictionary[word] = len(dictionary)\n",
    "  data = list()\n",
    "  unk_count = 0\n",
    "  for word in words:\n",
    "    index = dictionary.get(word, 0)\n",
    "    if index == 0:  # dictionary['UNK']\n",
    "      unk_count += 1\n",
    "    data.append(index)\n",
    "  count[0][1] = unk_count\n",
    "  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
    "  return data, count, dictionary, reversed_dictionary\n",
    "\n",
    "# Filling 4 global variables:\n",
    "# data - list of codes (integers from 0 to vocabulary_size-1).\n",
    "#   This is the original text but words are replaced by their codes\n",
    "# count - map of words(strings) to count of occurrences\n",
    "# dictionary - map of words(strings) to their codes(integers)\n",
    "# reverse_dictionary - maps codes(integers) to words(strings)\n",
    "data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,\n",
    "                                                            vocabulary_size)\n",
    "del vocabulary  # Hint to reduce memory.\n",
    "\n",
    "\n",
    "with open(\"dictionary.json\",\"w\") as inf:\n",
    "    json.dump(dictionary,inf,ensure_ascii=False, indent=4)\n",
    "\n",
    "with open(\"reverse_dictionary.json\",\"w\") as inf:\n",
    "    json.dump(reverse_dictionary,inf,ensure_ascii=False, indent=4)\n",
    "    \n",
    "    \n",
    "print('Most common words (+UNK)', count[:5])\n",
    "print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])\n",
    "\n",
    "data_index = 0\n",
    "\n",
    "\n",
    "\n",
    "# Step 3: Function to generate a training batch for the skip-gram model.\n",
    "def generate_batch(batch_size, num_skips, skip_window):\n",
    "  global data_index\n",
    "  assert batch_size % num_skips == 0\n",
    "  assert num_skips <= 2 * skip_window\n",
    "  batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "  span = 2 * skip_window + 1  # [ skip_window target skip_window ]\n",
    "  buffer = collections.deque(maxlen=span)\n",
    "  if data_index + span > len(data):\n",
    "    data_index = 0\n",
    "  buffer.extend(data[data_index:data_index + span])\n",
    "  data_index += span\n",
    "  for i in range(batch_size // num_skips):\n",
    "    context_words = [w for w in range(span) if w != skip_window]\n",
    "    words_to_use = random.sample(context_words, num_skips)\n",
    "    for j, context_word in enumerate(words_to_use):\n",
    "      batch[i * num_skips + j] = buffer[skip_window]\n",
    "      labels[i * num_skips + j, 0] = buffer[context_word]\n",
    "    if data_index == len(data):\n",
    "      print(\"!!!!!!!!!!data_index!!!!!!!!!!!!!!!!!!!!!!:\"+str(data_index))\n",
    "      print(\"!!!!!!!!!!span!!!!!!!!!!!!!!!!!!!!!!:\"+str(span))\n",
    "      print(buffer)\n",
    "      #buffer[:] = data[:span]\n",
    "      for word in data[:span]:\n",
    "        buffer.append(word)\n",
    "      data_index = span\n",
    "    else:\n",
    "      buffer.append(data[data_index])\n",
    "      data_index += 1\n",
    "  # Backtrack a little bit to avoid skipping words in the end of a batch\n",
    "  data_index = (data_index + len(data) - span) % len(data)\n",
    "  return batch, labels\n",
    "\n",
    "batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)\n",
    "for i in range(8):\n",
    "  print(batch[i], reverse_dictionary[batch[i]],\n",
    "        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])\n",
    "\n",
    "# Step 4: Build and train a skip-gram model.\n",
    "\n",
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "num_sampled = 64      # Number of negative examples to sample.\n",
    "\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. These 3 variables are used only for\n",
    "# displaying model accuracy, they don't affect calculation.\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n",
    "\n",
    "\n",
    "graph = tf.Graph()\n",
    "\n",
    "with graph.as_default():\n",
    "\n",
    "  # Input data.\n",
    "  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
    "\n",
    "  # Ops and variables pinned to the CPU because of missing GPU implementation\n",
    "  with tf.device('/gpu:0'):\n",
    "    # Look up embeddings for inputs.\n",
    "    embeddings = tf.Variable(\n",
    "        tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "    embed = tf.nn.embedding_lookup(embeddings, train_inputs)\n",
    "\n",
    "    # Construct the variables for the NCE loss\n",
    "    nce_weights = tf.Variable(\n",
    "        tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                            stddev=1.0 / math.sqrt(embedding_size)))\n",
    "    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
    "\n",
    "  # Compute the average NCE loss for the batch.\n",
    "  # tf.nce_loss automatically draws a new sample of the negative labels each\n",
    "  # time we evaluate the loss.\n",
    "  # Explanation of the meaning of NCE loss:\n",
    "  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/\n",
    "  loss = tf.reduce_mean(\n",
    "      tf.nn.nce_loss(weights=nce_weights,\n",
    "                     biases=nce_biases,\n",
    "                     labels=train_labels,\n",
    "                     inputs=embed,\n",
    "                     num_sampled=num_sampled,\n",
    "                     num_classes=vocabulary_size))\n",
    "\n",
    "  # Construct the SGD optimizer using a learning rate of 1.0.\n",
    "  optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n",
    "\n",
    "  # Compute the cosine similarity between minibatch examples and all embeddings.\n",
    "  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "  normalized_embeddings = embeddings / norm\n",
    "  valid_embeddings = tf.nn.embedding_lookup(\n",
    "      normalized_embeddings, valid_dataset)\n",
    "  similarity = tf.matmul(\n",
    "      valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "\n",
    "  # Add variable initializer.\n",
    "  init = tf.global_variables_initializer()\n",
    "\n",
    "# Step 5: Begin training.\n",
    "num_steps = 100001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "  # We must initialize all variables before we use them.\n",
    "  init.run()\n",
    "  print('Initialized')\n",
    "\n",
    "  average_loss = 0\n",
    "  for step in xrange(num_steps):\n",
    "    batch_inputs, batch_labels = generate_batch(\n",
    "        batch_size, num_skips, skip_window)\n",
    "    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "\n",
    "    # We perform one update step by evaluating the optimizer op (including it\n",
    "    # in the list of returned values for session.run()\n",
    "    _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)\n",
    "    average_loss += loss_val\n",
    "\n",
    "    if step % 2000 == 0:\n",
    "      if step > 0:\n",
    "        average_loss /= 2000\n",
    "      # The average loss is an estimate of the loss over the last 2000 batches.\n",
    "      print('Average loss at step ', step, ': ', average_loss)\n",
    "      average_loss = 0\n",
    "\n",
    "    # Note that this is expensive (~20% slowdown if computed every 500 steps)\n",
    "    if step % 20000 == 0:\n",
    "      sim = similarity.eval()\n",
    "      for i in xrange(valid_size):\n",
    "        valid_word = reverse_dictionary[valid_examples[i]]\n",
    "        top_k = 8  # number of nearest neighbors\n",
    "        nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "        print(nearest)\n",
    "        log_str = 'Nearest to %s:' % valid_word\n",
    "        for k in xrange(top_k):\n",
    "          if nearest[k] in reverse_dictionary.keys():\n",
    "              close_word = reverse_dictionary[nearest[k]]\n",
    "              log_str = '%s %s,' % (log_str, close_word)\n",
    "              print(log_str)\n",
    "  final_embeddings = normalized_embeddings.eval()\n",
    "\n",
    "# Step 6: Visualize the embeddings.\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#指定默认字体\n",
    "matplotlib.rcParams['font.sans-serif'] = ['SimHei']\n",
    "matplotlib.rcParams['font.family']='sans-serif'\n",
    "#解决负号'-'显示为方块的问题\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# pylint: disable=missing-docstring\n",
    "# Function to draw visualization of distance between embeddings.\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  plt.figure(figsize=(18, 18))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = low_dim_embs[i, :]\n",
    "    plt.scatter(x, y)\n",
    "    print(\"(x, y)=(%s,%s)\",str(x),str(y))\n",
    "    print(\"label=\"+label)\n",
    "    \n",
    "    #plt.title(u'matplotlib中文显示测试——Tacey Wong',fontproperties=myfont)\n",
    "    #plt.xlabel(u'这里是X坐标',fontproperties=myfont)\n",
    "    #plt.ylabel(u'这里是Y坐标',fontproperties=myfont)\n",
    "    plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "\n",
    "  plt.savefig(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(x, y)=(%s,%s) 8.033749 -44.311993\n",
      "label=UNK\n",
      "(x, y)=(%s,%s) 5.65725 0.16439931\n",
      "label=。\n",
      "(x, y)=(%s,%s) 3.8241284 -0.85682607\n",
      "label=\n",
      "\n",
      "(x, y)=(%s,%s) 7.1916804 0.54376227\n",
      "label=，\n",
      "(x, y)=(%s,%s) 7.1888375 1.0503366\n",
      "label=、\n",
      "(x, y)=(%s,%s) -24.716133 -1.6097848\n",
      "label=人\n",
      "(x, y)=(%s,%s) 4.615179 30.787947\n",
      "label=花\n",
      "(x, y)=(%s,%s) 3.985836 1.6046785\n",
      "label=风\n",
      "(x, y)=(%s,%s) -4.4735246 -3.21987\n",
      "label=一\n",
      "(x, y)=(%s,%s) 34.495323 -29.148659\n",
      "label=（\n",
      "(x, y)=(%s,%s) -36.60294 18.60552\n",
      "label=）\n",
      "(x, y)=(%s,%s) -1.5387945 27.229366\n",
      "label=春\n",
      "(x, y)=(%s,%s) -6.796399 4.9913464\n",
      "label=不\n",
      "(x, y)=(%s,%s) 13.514439 -4.5493145\n",
      "label=天\n",
      "(x, y)=(%s,%s) -12.792301 15.514483\n",
      "label=月\n",
      "(x, y)=(%s,%s) -5.4551263 1.6827831\n",
      "label=无\n",
      "(x, y)=(%s,%s) -31.394709 -17.258186\n",
      "label=云\n",
      "(x, y)=(%s,%s) -27.151154 -15.384653\n",
      "label=山\n",
      "(x, y)=(%s,%s) -8.861182 -7.1322575\n",
      "label=来\n",
      "(x, y)=(%s,%s) 4.8191876 -18.308533\n",
      "label=有\n",
      "(x, y)=(%s,%s) 2.8482563 27.832653\n",
      "label=香\n",
      "(x, y)=(%s,%s) 0.6716556 12.325691\n",
      "label=时\n",
      "(x, y)=(%s,%s) 25.387995 23.377073\n",
      "label=江\n",
      "(x, y)=(%s,%s) -0.43916997 13.884092\n",
      "label=年\n",
      "(x, y)=(%s,%s) -25.355682 -14.981499\n",
      "label=水\n",
      "(x, y)=(%s,%s) -13.811185 15.537486\n",
      "label=日\n",
      "(x, y)=(%s,%s) -18.157616 47.690426\n",
      "label=玉\n",
      "(x, y)=(%s,%s) 7.4228234 7.462963\n",
      "label=清\n",
      "(x, y)=(%s,%s) -8.644275 -22.288525\n",
      "label=如\n",
      "(x, y)=(%s,%s) 4.880885 -19.588017\n",
      "label=是\n",
      "(x, y)=(%s,%s) -7.198116 2.0489337\n",
      "label=何\n",
      "(x, y)=(%s,%s) 5.9557962 31.440233\n",
      "label=红\n",
      "(x, y)=(%s,%s) -10.956676 -8.765649\n",
      "label=归\n",
      "(x, y)=(%s,%s) 1.6750627 4.2618666\n",
      "label=处\n",
      "(x, y)=(%s,%s) 5.646899 -3.5417333\n",
      "label=相\n",
      "(x, y)=(%s,%s) 10.006976 -30.372604\n",
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      "label=雨\n",
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      "label=去\n",
      "(x, y)=(%s,%s) -2.0809796 -37.672043\n",
      "label=生\n",
      "(x, y)=(%s,%s) 41.092907 -14.62129\n",
      "label=酒\n",
      "(x, y)=(%s,%s) 21.403059 11.81413\n",
      "label=长\n",
      "(x, y)=(%s,%s) -20.415312 48.115208\n",
      "label=金\n",
      "(x, y)=(%s,%s) -8.827177 7.036221\n",
      "label=谁\n",
      "(x, y)=(%s,%s) -2.0047383 26.581095\n",
      "label=秋\n",
      "(x, y)=(%s,%s) 43.550224 13.844424\n",
      "label=歌\n",
      "(x, y)=(%s,%s) 13.367239 -24.680504\n",
      "label=里\n",
      "(x, y)=(%s,%s) 24.346077 8.257171\n",
      "label=子\n",
      "(x, y)=(%s,%s) -15.277025 -14.2247715\n",
      "label=醉\n",
      "(x, y)=(%s,%s) -44.42239 10.198478\n",
      "label=千\n",
      "(x, y)=(%s,%s) -16.792295 13.069222\n",
      "label=夜\n",
      "(x, y)=(%s,%s) 15.798974 -15.557214\n",
      "label=事\n",
      "(x, y)=(%s,%s) -7.6429424 -6.069266\n",
      "label=今\n",
      "(x, y)=(%s,%s) -1.9299779 23.470024\n",
      "label=寒\n",
      "(x, y)=(%s,%s) -13.633218 -6.4491715\n",
      "label=梦\n",
      "(x, y)=(%s,%s) 23.983192 30.602657\n",
      "label=飞\n",
      "(x, y)=(%s,%s) 27.441992 1.7352692\n",
      "label=西\n",
      "(x, y)=(%s,%s) -6.7361774 -13.422693\n",
      "label=明\n",
      "(x, y)=(%s,%s) -8.674498 17.035585\n",
      "label=新\n",
      "(x, y)=(%s,%s) 20.72046 -13.917955\n",
      "label=情\n",
      "(x, y)=(%s,%s) 28.134367 1.9300343\n",
      "label=东\n",
      "(x, y)=(%s,%s) 3.6821408 -44.779175\n",
      "label=得\n",
      "(x, y)=(%s,%s) 20.157486 -15.096884\n",
      "label=心\n",
      "(x, y)=(%s,%s) 18.894125 -11.834475\n",
      "label=愁\n",
      "(x, y)=(%s,%s) 2.2503483 -4.9149666\n",
      "label=更\n",
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      "label=满\n",
      "(x, y)=(%s,%s) -35.203094 5.7150335\n",
      "label=小\n",
      "(x, y)=(%s,%s) -6.4178877 5.506462\n",
      "label=未\n",
      "(x, y)=(%s,%s) -1.6172731 -7.134139\n",
      "label=自\n",
      "(x, y)=(%s,%s) -31.67897 -18.28273\n",
      "label=烟\n",
      "(x, y)=(%s,%s) -13.73503 -39.435707\n",
      "label=声\n",
      "(x, y)=(%s,%s) 29.439478 -0.40182\n",
      "label=南\n",
      "(x, y)=(%s,%s) -14.388367 34.703705\n",
      "label=楼\n",
      "(x, y)=(%s,%s) 6.2819834 -14.457\n",
      "label=为\n",
      "(x, y)=(%s,%s) -3.2553644 36.142956\n",
      "label=青\n",
      "(x, y)=(%s,%s) -18.101713 20.126337\n",
      "label=重\n",
      "(x, y)=(%s,%s) 24.488205 0.656975\n",
      "label=仙\n",
      "(x, y)=(%s,%s) 13.158291 -5.8519726\n",
      "label=空\n",
      "(x, y)=(%s,%s) 8.995967 -25.377998\n",
      "label=前\n",
      "(x, y)=(%s,%s) -1.9889785 -16.899664\n",
      "label=看\n",
      "(x, y)=(%s,%s) 12.270388 -32.52251\n",
      "label=头\n",
      "(x, y)=(%s,%s) -15.207903 -15.114489\n",
      "label=笑\n",
      "(x, y)=(%s,%s) 8.18195 34.813736\n",
      "label=梅\n",
      "(x, y)=(%s,%s) -5.6040916 -5.641073\n",
      "label=此\n",
      "(x, y)=(%s,%s) -5.062561 -36.48106\n",
      "label=老\n",
      "(x, y)=(%s,%s) -4.0331216 -24.84048\n",
      "label=见\n",
      "(x, y)=(%s,%s) -7.862674 -22.09411\n",
      "label=似\n",
      "(x, y)=(%s,%s) 5.4363003 -14.109861\n",
      "label=与\n",
      "(x, y)=(%s,%s) 15.628344 11.91864\n",
      "label=行\n",
      "(x, y)=(%s,%s) -44.861404 2.433672\n",
      "label=三\n",
      "(x, y)=(%s,%s) 5.0986085 17.103895\n",
      "label=尽\n",
      "(x, y)=(%s,%s) 17.772545 8.012828\n",
      "label=知\n",
      "(x, y)=(%s,%s) 3.6057632 6.256665\n",
      "label=好\n",
      "(x, y)=(%s,%s) -6.1195006 -41.55167\n",
      "label=流\n",
      "(x, y)=(%s,%s) -16.657825 26.433157\n",
      "label=深\n",
      "(x, y)=(%s,%s) -8.28229 9.785264\n",
      "label=又\n",
      "(x, y)=(%s,%s) 10.459601 29.065546\n",
      "label=柳\n",
      "(x, y)=(%s,%s) -6.717886 -0.6234865\n",
      "label=多\n",
      "(x, y)=(%s,%s) 23.453465 28.744314\n",
      "label=落\n",
      "(x, y)=(%s,%s) 6.5770607 -7.726378\n",
      "label=君\n",
      "(x, y)=(%s,%s) 32.446926 15.924336\n",
      "label=黄\n",
      "(x, y)=(%s,%s) 3.4282749 -20.952787\n",
      "label=在\n",
      "(x, y)=(%s,%s) -44.324947 10.757231\n",
      "label=万\n",
      "(x, y)=(%s,%s) 1.9457347 29.16161\n",
      "label=芳\n",
      "(x, y)=(%s,%s) 3.4832613 3.9395788\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14b0105470>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "try:\n",
    "  # pylint: disable=g-import-not-at-top\n",
    "  from sklearn.manifold import TSNE\n",
    "  import matplotlib.pyplot as plt\n",
    "\n",
    "  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "  plot_only = 500\n",
    "  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "  labels = [reverse_dictionary[i] for i in xrange(plot_only)]\n",
    "  plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))\n",
    "except ImportError as ex:\n",
    "  print('Please install sklearn, matplotlib, and scipy to show embeddings.')\n",
    "  print(ex)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
